Web-Scale Responsive Visual Search at Bing
| Autor: | Xi Chen, Meenaz Merchant, Huang Jiapei, Houdong Hu, Yan Wang, Wu Ye, Arun Sacheti, Linjun Yang, Pavel Komlev, Huang Li |
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| Rok vydání: | 2018 |
| Předmět: |
Visual search
FOS: Computer and information sciences Computer science business.industry Deep learning Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Symmetric multiprocessor system 02 engineering and technology Content-based image retrieval Object detection Human–computer interaction 020204 information systems Scalability 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | KDD |
| DOI: | 10.48550/arxiv.1802.04914 |
| Popis: | In this paper, we introduce a web-scale general visual search system deployed in Microsoft Bing. The system accommodates tens of billions of images in the index, with thousands of features for each image, and can respond in less than 200 ms. In order to overcome the challenges in relevance, latency, and scalability in such large scale of data, we employ a cascaded learning-to-rank framework based on various latest deep learning visual features, and deploy in a distributed heterogeneous computing platform. Quantitative and qualitative experiments show that our system is able to support various applications on Bing website and apps. |
| Databáze: | OpenAIRE |
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